1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Test 4 — 15 Questions

Question 1

A data scientist fine-tunes a model using OCI Data Science and wants to deploy it as a managed endpoint in OCI Generative AI. What must they do first?

Accepted Answer

Upload model artifacts to Object Storage and register in Model Catalog. To deploy a fine-tuned model as a managed endpoint in OCI Generative AI, the model artifacts must first be uploaded to Object Storage and registered in the Model Catalog. This is a prerequisite because OCI Generative AI endpoints pull model artifacts from the Model Catalog, which references the storage location. Without registration, the service cannot locate or serve the model.

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Write a custom container

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Create a dedicated AI cluster

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Use OCI CLI to create an endpoint

Question 2

An organization wants to use OCI Generative AI to build a summarization tool but must ensure that all inference requests are logged for audit purposes. Which approach should they take?

Accepted Answer

Enable OCI Logging on the generative AI endpoint. OCI Logging can be enabled directly on the Generative AI endpoint to capture all inference requests and responses as logs, which can then be used for audit purposes. This is the native, recommended approach for logging API calls without introducing additional infrastructure or complexity.

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Implement a custom proxy with logging

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Enable OCI Audit service

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Use OCI Vault to store logs

Question 3

A company deploys a large language model on a dedicated AI cluster with 4 nodes. The model requires 128 GB of memory per instance, but the nodes have only 64 GB each. During inference, the nodes experience out-of-memory errors. What is the best solution?

Accepted Answer

Enable model parallelism across nodes. Model parallelism splits the model's layers or parameters across multiple nodes, allowing the 128 GB model to be distributed across the 4 nodes (each with 64 GB) so that no single node exceeds its memory capacity. This is the best solution because it directly addresses the memory constraint without requiring additional hardware or sacrificing inference throughput, and it is a standard technique for deploying large language models on distributed AI clusters.

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Increase the number of nodes to 8

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Upgrade to higher memory node shapes

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Reduce the batch size in inference requests

Question 4

A financial company deploys a generative AI model for document analysis. They need to ensure that the model does not expose sensitive information in its responses. Which OCI service should they use to implement content filtering?

Accepted Answer

OCI AI Content Moderation. OCI AI Content Moderation is the correct service because it provides pre-trained models and APIs specifically designed to detect and filter sensitive content such as personally identifiable information (PII), profanity, and other unsafe text in generative AI outputs. This allows the financial company to enforce content safety policies on document analysis responses, preventing exposure of sensitive information.

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OCI Data Safe

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OCI Vault

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OCI WAF

Question 5

A user wants to invoke an OCI Generative AI endpoint from a cloud function. What is the required authentication method?

Accepted Answer

API signing key. OCI Generative AI endpoints require API signing keys for authentication because they are REST APIs that use the Signature Version 1 algorithm (based on HMAC-SHA256) to sign requests. Cloud Functions must include a signed HTTP header using a user's or service principal's OCI API signing key pair (private key for signing, public key uploaded to OCI) to prove identity and authorization. This is the standard method for programmatic access to OCI services, including Generative AI, and is enforced by the OCI Identity and Access Management (IAM) policy layer.

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User name and password

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Session token

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OCI certificate

Question 6

Which TWO are best practices for securing a generative AI endpoint on OCI? (Select TWO)

Accepted Answer

Store API keys in OCI Vault. Option D is correct because storing API keys and other secrets in OCI Vault centralizes secret management with encryption at rest, fine-grained IAM policies, and auditability, preventing hard-coded credentials in application code or config files. Option E is correct because deploying the generative AI endpoint on a dedicated AI cluster within a private subnet removes public internet exposure and enforces network isolation via security lists, NSGs, and private routing. Option A is not among the marked correct answers, and while logging is useful, it is a monitoring/audit control rather than a primary endpoint-hardening best practice for this scenario. Option B is not correct because a public endpoint, even with IP restrictions, still exposes the service to the internet and is weaker than a private subnet deployment. Option C is not correct because disabling authentication for internal use violates zero-trust principles and would allow any internal actor to invoke the endpoint without authorization.

Answer

Enable OCI Logging for audit

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Use a public endpoint with IP restrictions

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Disable authentication for internal use

Question 7

Which THREE components are essential for a production-grade generative AI deployment on OCI? (Select THREE)

Accepted Answer

OCI Logging for audit. OCI Logging (A) is essential because it captures audit logs of API calls and model invocations, providing the traceability and compliance evidence required in a production generative AI deployment. A dedicated AI cluster (D) is required to host and serve the generative AI model with guaranteed, isolated compute capacity and consistent performance rather than relying on shared, best-effort infrastructure. OCI IAM policies (E) are essential to enforce least-privilege access control over the model endpoints, data, and related OCI resources, which is a baseline security requirement for production workloads. OCI Vault (B) is valuable for storing secrets but is not one of the three components the question treats as essential, and OCI Data Flow (C) is a batch data-processing service that is not mandatory for serving a generative AI model.

Answer

OCI Vault for secrets

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OCI Data Flow for data processing

Question 8

A data science team at a healthcare company has fine-tuned a Llama 2 model using OCI Data Science and registered it in the Model Catalog. They want to deploy it as a managed endpoint using OCI Generative AI. The model requires 64 GB of GPU memory. The team has created a dedicated AI cluster with a single node shape that has 48 GB GPU memory. When they attempt to deploy the model, the deployment fails with an error indicating insufficient resources. The team has verified that the model artifact is correct and that the compartment policies allow deployment. What should the team do to successfully deploy the model?

Accepted Answer

Select a node shape with higher GPU memory, such as 80 GB.. The model requires 64 GB of GPU memory, but the dedicated AI cluster uses a node shape with only 48 GB. The only way to satisfy the memory requirement is to select a node shape with higher GPU memory, such as 80 GB, as OCI Generative AI managed endpoints require a single node to host the entire model. Increasing nodes or enabling model parallelism does not help because OCI Generative AI does not support distributed inference across nodes for managed endpoints, and reducing precision may not guarantee the model fits or may degrade accuracy.

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Increase the number of nodes in the cluster to 2.

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Enable model parallelism to split the model across nodes.

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Reduce the model's precision from FP16 to INT8 to lower memory usage.

Question 9

A data scientist deployed a fine-tuned Llama 2 7B model on OCI Model Deployment with a single VM.GPU.A10.1 shape. Users report average latency of 3 seconds per request, which is too high for the intended real-time application. The model is used for short text generation (max 128 tokens). The data scientist wants to reduce per-request latency without significant accuracy loss. Which action would be most effective?

Accepted Answer

Apply 4-bit quantization using AWQ. 4-bit quantization using AWQ reduces the model's memory footprint and computational requirements by compressing weights to 4-bit integers, which directly decreases inference latency on the VM.GPU.A10.1 shape. This technique preserves most of the model's accuracy while enabling faster token generation, making it the most effective single action for reducing per-request latency in a real-time short text generation scenario.

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Increase the number of workers per replica

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Increase the max_tokens parameter for the model

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Enable response streaming for the model endpoint

Question 10

A company wants to deploy a custom generative AI model for generating synthetic data for training other models. The model requires approximately 20GB of memory and must be accessible via a REST API with authentication. Additionally, the team needs to monitor for data drift over time. Which combination of OCI services best meets these requirements with minimal operational overhead?

Accepted Answer

OCI Data Science Model Deployment with OCI Monitoring and OCI Logging. OCI Data Science Model Deployment provides a managed environment for hosting custom generative AI models with REST API endpoints and built-in authentication via OCI IAM. It integrates natively with OCI Monitoring and OCI Logging to track data drift and operational metrics without requiring additional infrastructure setup, minimizing operational overhead.

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OCI Compute with custom Docker container and Prometheus monitoring

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OCI Functions with API Gateway for authentication

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OCI Data Flow with OCI Data Catalog for model registry

Question 11

Your organization uses OCI Data Science to train a generative AI model for code generation. After training, you want to deploy it as a REST API. You create a model deployment using the OCI console, but after 30 minutes the deployment status is still 'Creating'. You check the logs and see the message: 'Insufficient capacity for shape VM.GPU.A10.1 in availability domain AD-1'. The deployment is configured with a single replica. You have verified your tenancy has sufficient service limits for GPU instances. What should you do to resolve this issue quickly?

Accepted Answer

Change the deployment to use a different GPU shape, such as VM.GPU.A10.2. The error indicates that the specific GPU shape VM.GPU.A10.1 lacks capacity in the current availability domain. Switching to a different GPU shape, such as VM.GPU.A10.2, which uses a different instance configuration, can bypass the capacity constraint without requiring a region change or service limit increase. This is the fastest resolution because it directly addresses the availability domain capacity issue while keeping the deployment in the same region and AD.

Answer

Delete the deployment and create it in a different region with more GPU capacity

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Request a service limit increase for GPU shapes

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Wait for 1 hour and check again; capacity may become available

Question 12

You deployed a generative AI model on OCI Model Deployment with autoscaling configured based on average CPU utilization. The model is a large language model that heavily utilizes the GPU. During peak hours, the scaling is too slow to keep up with demand, resulting in high latency for users. You want to improve the responsiveness of autoscaling. Which change should you make?

Accepted Answer

Use GPU utilization as the scaling metric instead of CPU utilization. The model heavily utilizes GPU, not CPU. Autoscaling based on CPU utilization is irrelevant for GPU-bound workloads, leading to delayed scale-out. Using GPU utilization as the scaling metric directly reflects the actual resource bottleneck, enabling faster and more accurate scaling decisions.

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Decrease the target CPU utilization threshold for scale-out

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Increase the maximum number of replicas in the autoscaling configuration

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Increase the cooldown period between scale-out events

Question 13

A generative AI model deployed on OCI Model Deployment is experiencing high tail latency. The model is a large language model that processes variable-length input sequences. Profiling shows that inference time varies significantly: short inputs (100 tokens) take 100ms, while long inputs (2000 tokens) take 2 seconds. The application requires consistent low latency (<500ms) for most requests. You want to reduce the variance in inference time without major changes to the model architecture. Which technique should you apply?

Accepted Answer

Implement dynamic batching that groups requests of similar lengths together before inference. Dynamic batching groups requests of similar input lengths together, which reduces the variance in inference time by ensuring that each batch processes tokens of comparable size. This minimizes the padding overhead and keeps the per-request latency more predictable, directly addressing the high tail latency caused by variable-length sequences without altering the model architecture.

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Increase the number of replicas to distribute the load evenly

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Reduce the model size by removing layers or using a smaller version

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Deploy multiple model endpoints for different length ranges and route requests accordingly

Question 14

Your organization has deployed a generative AI model for a multilingual translation service on OCI Model Deployment. The model is a 13B parameter transformer hosted on a single VM.GPU.A100.1 shape with 2 replicas. Recently, the service experiences intermittent timeouts when a burst of requests arrives. You have enabled autoscaling based on CPU utilization, but the scaling is too slow. After investigation, you find that the model inference time is highly variable due to different sequence lengths. You need to ensure the service can handle sudden spikes without timeouts. Which solution should you implement?

Accepted Answer

Implement a request queue (e.g., OCI Queue) to buffer requests and process them asynchronously. Implementing a request queue (e.g., OCI Queue) decouples request ingestion from processing, allowing the service to buffer bursts of requests and process them asynchronously. This prevents timeouts by smoothing out the variable inference times caused by differing sequence lengths, as the queue absorbs spikes and the model processes at its own pace. Autoscaling based on CPU utilization is too slow for sudden spikes, but a queue provides immediate relief by not dropping requests.

Answer

Increase the maximum number of replicas and prewarm additional replicas before expected traffic

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Reduce the model size to a 7B parameter model to decrease inference time

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Use autoscaling based on the number of messages in the request queue

Question 15

A team is deploying a fine-tuned model to an OCI Generative AI dedicated AI cluster. They need to ensure that inference requests are distributed evenly across the cluster's endpoints and that the application can automatically retry failed requests. Which OCI Generative AI feature should they configure?

Accepted Answer

The dedicated AI cluster's endpoint with client-side load balancing and retry policies in the application. OCI Generative AI dedicated AI clusters expose a single endpoint that automatically distributes inference requests across the model replicas within the cluster. The application should use client-side retry logic to handle transient failures. This leverages the service's built-in load balancing and avoids unnecessary external components. The correct approach is to use the cluster endpoint directly and configure retries in the application code.

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A load balancer configured with the dedicated AI cluster's endpoint as a backend set

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Multiple endpoints on the dedicated AI cluster with a load balancer in front

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The dedicated AI cluster's built-in endpoint with automatic scaling and retry logic in the SDK